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List of Publications

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2008

Linearly-Solvable Markov Decision Processes [21]

Uhlich, M.

2008

Link to publication [22] Download Bibtex entry [23]

2009

Improving on Expectation Propagation [24]

Opper, M., Paquet, U. and Winther, O.

Advances in Neural Information Processing Systems 21. MIT Press. 2009

Download Bibtex entry [25]

Approximate inference for stochastic reaction processes [26]

Ruttor, A., Sanguinetti, G. and Opper, M.

Learning and Inference in Computational Systems Biology. The MIT Press, 189–205. 2009

Link to publication [27] Download Bibtex entry [28]

Efficient Statistical Inference for Stochastic Reaction Processes [29]

Ruttor, A. and Opper, M.

Phys. Rev. Lett., 230601. 2009

Link to publication [30] Download Bibtex entry [31]

Switching Regulatory Models of Cellular Stress Response [32]

Sanguinetti, G., Ruttor, A., Opper, M. and Archambeau, C.

Bioinformatics, 1280-1286. 2009

Link to publication [33] Download Bibtex entry [34]

Lösung eines Kontrollproblems mittels Gaußscher Prozesse [35]

Bronstein, L.

2009 TU Berlin

Link to publication [36] Download Bibtex entry [37]

Diffusion Approximation for Bayesian Inference on Chemical Reaction Systems [38]

Stimberg, F.

2009

Link to publication [39] Download Bibtex entry [40]

Switching Regulatory Models of Cellular Stress Response [41]

Sanguinetti, G., Ruttor, A., Opper, M. and Archambeau, C.

Bioinformatics 2009

Download Bibtex entry [42]

The Variational Gaussian Approximation Revisited [43]

Archambeau, C. and Opper, M.

Neural Computation, 786-92. 2009

Download Bibtex entry [44]

A comparison of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems [45]

Shen, Y., Archambeau, C., Cornford, D., Opper, M., Shawe-Taylor, J. and Barillec, R.

Journal of Signal Processing Systems 2009

Link to publication [46] Download Bibtex entry [47]

2010

Das variationale Dirichlet Prozess Mixture Modell [48]

Batz, P.

2010 HU Berlin

Link to publication [49] Download Bibtex entry [50]

Approximate inference for stochastic reaction processes [51]

Ruttor, A., Sanguinetti, G. and Opper, M.

Learning and Inference in Computational Systems Biology. MIT Press, 189-205. 2010

Link to publication [52] Download Bibtex entry [53]

Approximate parameter inference in a stochastic reaction-diffusion model [54]

Ruttor, A. and Opper, M.

Proceedings of The Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2010. JMLR, 669-676. 2010

Link to publication [55] Download Bibtex entry [56]

Approximate inference in continuous time Gaussian-Jump processes [57]

Opper, M., Ruttor, A. and Sanguinetti, G.

Advances in Neural Information Processing Systems 23, 1831–1839. 2010

Link to publication [58] Download Bibtex entry [59]

Comparing diffusion and weak noise approximations for inference in reaction models [60]

Ruttor, A., Stimberg, F. and Opper, M.

Proceedings of the Fourth International Workshop on Machine Learning in Systems Biology (October 15-16, 2010, Edinburgh, UK), 149–152. 2010

Link to publication [61] Download Bibtex entry [62]

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